PNNL

PNNL contributions to GE's Positive Sequence Load Flow tool enhance power grid simulation and prediction capabilities.

The nation's complex power grid requires speedy, informed decision-making, both for day-to-day operations and longer-term planning.

In a recent project, Pacific Northwest National Laboratory researchers, in concert with counterparts at General Electric and Southern California Edison, developed and applied high-performance computing (HPC) techniques to increase the speed of GE’s Positive Sequence Load Flow (PSLF) dynamic simulation tool by nearly 200 percent. The improvements move PSLF closer to real-time simulations, which enhance the ability to predict grid operations. The two-year effort was funded by the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability.

Algorithm Adapted to Improve Tool

A longstanding grid industry resource, PSLF reflects more than three decades of development. In addition to dynamic simulation, the tool performs power flow and contingency analyses. GE felt advanced computing capabilities, applied to the tool, could make PSLF faster, strengthening the impact of dynamic simulation, benefitting grid operations and planning and, ultimately, helping prevent blackouts and other failures.

A key PNNL contribution to the project involved identification of an algorithm—known as a fast linear solver—for incorporation into PSLF. After surveying industry, universities and other organizations to find and evaluate promising candidates, PNNL researchers identified one that seemed a good fit for PSLF, and helped GE adapt the solver to the tool. GE tested the enhanced PSLF product on its own workstations, and then remotely accessed a PNNL Institutional Computing resource, the BigMem computer, for further verification and validation.

The updated solver generously increased PSLF’s overall speed. “Testing demonstrated that the speedup was twice as fast using four computer processors, and subsequent evaluation has confirmed the increase,” says PNNL’s Ruisheng Diao, adding, “This kind of advance is important because it reduces the amount of time needed to determine the best solutions for addressing a range of grid challenges.”